Results 31 to 40 of about 1,703,706 (235)
An improved unbiased particle filter
Monte Carlo Methods and Applications, 2023 Abstract In this paper, we consider the filtering of partially observed multi-dimensional diffusion processes that are observed regularly at discrete times. We assume that, for numerical reasons, one has to time-discretize the diffusion process, which typically leads to filtering that is subject to discretization bias. The approach in [A.
Ajay Jasra +2 more
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Resampling Algorithms for Particle Filters: A Computational Complexity Perspective
EURASIP Journal on Advances in Signal Processing, 2004 Newly developed resampling algorithms for particle filters suitable for real-time implementation are described and their analysis is presented. The new algorithms reduce the complexity of both hardware and DSP realization through addressing common issues
Miodrag Bolić +2 more
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Saturated particle filter [PDF]
Proceedings of the 2011 American Control Conference, 2011 In many practical applications the state variables are defined on a compact set of the state space. For estimating such variables constrained particle filters have been successfully applied to nonlinear systems. For the saturated system the measurement information can be used during the sampling procedure to obtain particles that approximate the true ...
Pawel Stano +2 more
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Digital IIR filter design using particle swarm optimisation [PDF]
, 2010 Adaptive infinite-impulse-response (IIR) filtering provides a powerful approach for solving a variety of practical signal processing problems. Because the error surface of IIR filters is typically multimodal, global optimisation techniques are generally ...
Chen, Sheng, Luk, Bing L.
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Particle filters for combined state and parameter estimation. [PDF]
, 2001 Filtering is a method of estimating the conditional probability distribution of a signal based upon a noisy, partial, corrupted sequence of observations of the signal.
Chan, Hubert, Kouritzin, Michael
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MapReduce particle filtering with exact resampling and deterministic runtime
EURASIP Journal on Advances in Signal Processing, 2017 Particle filtering is a numerical Bayesian technique that has great potential for solving sequential estimation problems involving non-linear and non-Gaussian models.
Jeyarajan Thiyagalingam +2 more
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Particle Flow Gaussian Particle Filter
2022 25th International Conference on Information Fusion (FUSION), 2022 State estimation in non-linear models is performed by tracking the posterior distribution recursively. A plethora of algorithms have been proposed for this task. Among them, the Gaussian particle filter uses a weighted set of particles to construct a Gaussian approximation to the posterior.
Comandur, Karthik +2 more
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Indoor EnvironmentsElectrospun nanofiber air filters can achieve high particle filtration efficiency with lower pressure drop compared with high-efficiency particulate air (HEPA) filters. Therefore, they can potentially be used for effective indoor particle removal.
Zhuolun Niu, Chun Chen
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Joint Audio-Visual Tracking Using Particle Filters
EURASIP Journal on Advances in Signal Processing, 2002 It is often advantageous to track objects in a scene using multimodal information when such information is available. We use audio as a complementary modality to video data, which, in comparison to vision, can provide faster localization over a wider ...
Dmitry N. Zotkin +2 more
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A Unification of Weighted and Unweighted Particle Filters [PDF]
, 2022 Abedi E, Surace SC, Pfister J-P. A Unification of Weighted and Unweighted Particle Filters. SIAM Journal on Control and Optimization.
Pfister, Jean-Pascal +6 more
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